Traditional convolutional neural networks (tCNNs) often struggle with medical image analysis due to complex transformations and irregular structures with weak boundaries. Deformable convolutional neural networks (dCNNs) address these challenges through specialized modules, showing improvements in classification, segmentation, and explainability on general image tasks. However, dCNNs remain relatively unexplored in the medical domain. This study provides a unique quantitative comparison of explainability between tCNNs and dCNNs on medical image classification tasks, focusing on lung disease classification using chest X-rays (CXRs). We tested four models with varying degrees of deformability and generated saliency maps using Guided GradCAM. To quantify interpretability, we introduced a novel metric: continuous intersection over union (cIoU). While tCNNs demonstrated slightly better classification results, dCNNs showed significant improvements in explainability. We observed a positive correlation between the number of deformable layers in a model and the quality of its saliency maps. These findings highlight the potential of dCNNs in enhancing the interpretability of medical image analysis, which is crucial for real-world clinical research and practice.

Advanced deep-learning techniques have been employed to develop clinical decision support systems for diagnosis and prognosis using medical images. However, the presence of out-of-distribution (OOD) samples, which deviate from the training data distribution, poses a significant challenge. Accurate quantification of the predictive uncertainty is crucial for ensuring reliable and dependable implementation in medical settings as a clinical decision support system. In this work, we propose an ensemble model to derive predictive uncertainty estimates for uncertainty quantification on OOD medical imaging. Specifically, the models are initialized with ImageNet pre-trained weights and fine-tuned on chest Computed Tomography (CT). Moreover, we utilize Grad-CAM to visualize and interpret the areas of the image that contribute most to the model’s predictions and uncertainty estimates. This visualization technique enhances the in-terpretability of our ensemble model and supports more informed clinical decision-making. Through extensive experiments on three Chest CT datasets, we have demonstrated the effectiveness of our approach in estimating uncertainty under domain shifting. Our results provide valuable insights into the reliability and specificity of deep ensemble uncertainty predictions in medical image analysis. Our Uncertainty-Aware Ensemble (UAE) approach can enable reliable and transparent predictions for safety-critical medical applications.

Expert microscopic analysis of cells obtained from frequent heart biopsies is vital for early detection of pediatric heart transplant rejection to prevent heart failure. Detection of this rare condition is prone to low levels of expert agreement due to the difficulty of identifying subtle rejection signs within biopsy samples. The rarity of pediatric heart transplant rejection also means that very few gold-standard images are available for developing machine learning models. To solve this urgent clinical challenge, we developed a deep learning model to automatically quantify rejection risk within digital images of biopsied tissue using an explainable synthetic data augmentation approach. We developed this explainable AI framework to illustrate how our progressive and inspirational generative adversarial network models distinguish between normal tissue images and those containing cellular rejection signs. To quantify biopsy-level rejection risk, we first detect local rejection features using a binary image classifier trained with expert-annotated and synthetic examples. We converted these local predictions into a biopsy-wide rejection score via an interpretable histogram-based approach. Our model significantly improves upon prior works with the same dataset with an area under the receiver operating curve (AUROC) of 98.84% for the local rejection detection task and 95.56% for the biopsy-rejection prediction task. A biopsy-level sensitivity of 83.33% makes our approach suitable for early screening of biopsies to prioritize expert analysis. Our framework provides a solution to rare medical imaging challenges currently limited by small datasets.

According to the World Health Organization, Artificial Intelligence (AI) technology may assist in COVID-19 management. However, existing image segmentation using AI suffers from a lack of accuracy and explainability, which prevents its adoption in actual clinical practice. In this paper, we investigated an attention-based image segmentation method for COVID-19 CT imaging with enhanced interpretation capabilities. Specifically, we developed U-Net architecture-based for segmentation with attention coefficients to produce a salient feature map. We use the DICE score and accuracy to perform a comprehensive model evaluation. We compared to other well-known methods such as Light U-Net, COPLE-Net, and Res U-Net and demonstrated that attention U-Net is superior for COVID-19 segmentation tasks in terms of performance and explainability. We also developed the tool as a web-application with a graphic user interface with the goal to translate this AI-driven clinical decision-support system for real-world clinical use.

This paper reports an interpretable automated grading system for diabetic retinopathy using color fundus images. First, we develop shallow learners as baselines. Second, we pre-train deep neural networks to extract high-dimensional features and complex patterns from fundus images and utilize ensemble models to do automatic grading. Then we develop several explainable artificial intelligence models to visualize the extracted deep features and to interpret the predicted outcomes. We investigate the robustness of our system over two publicly available diabetic retinopathy fundus imaging datasets. In addition, we displayed both local and global explainable results to further illustrate the clinical decision-making process with deep models. The innovations of our work include (1) using ensemble models to boost the performance of diabetic retinopathy grading system, and (2) providing transparency of ensemble models using explainable artificial intelligence. The result has shown the potential to improve the effectiveness and accessibility of diabetic retinopathy screening in clinical practice and research settings.

Alzheimer's Disease (AD) is an irreversible and progressive neurodegenerative disorder with three stages: cognitively normal (CN), mild cognitive impairment (MCI), and clinical dementia. Progression and stage prediction of dementia plays an important role in prognosis and treatment. In this work, we developed a multi-modal AD progress prediction model that integrates magnetic resonance imaging (MRI) and electronic health record (EHR) to classify patients into three stages: CN, MCI, and AD. We trained deep auto-encoder to extract features from EHR data, and ResNet and 3D U-Net for MRI imaging data. We developed an entropy-based weighted sum classification method to integrate the classification results from each individual modality to generate final prediction. We experimented on Alzheimer's Disease Neuroimaging Initiative (ADNI) data to demonstrate that the multi-modality integration model outperforms single modality models in accuracy, precision, recall, and F1 scores. In addition, our model achieves competitive performance in comparison with other state-of-the-art multi-modality integration methods on AD progression prediction.